为了更加便利、快捷地在基于CT的肺部疾病诊断中获取相关肺功能指标,以便于辅助医生进行诊断,本研究开发了基于肺部组织分割的肺功能定量分析系统。系统主要分为三个部分:肺部组织的分割,肺部组织的三维重建以及肺功能定量分析模块。在...为了更加便利、快捷地在基于CT的肺部疾病诊断中获取相关肺功能指标,以便于辅助医生进行诊断,本研究开发了基于肺部组织分割的肺功能定量分析系统。系统主要分为三个部分:肺部组织的分割,肺部组织的三维重建以及肺功能定量分析模块。在利用已有方法分割出肺实质、肺气管和肺血管的基础上,提出了自动修补由肺结节造成的肺实质孔洞以及肺边缘缺陷的方法,利用基于海森矩阵的圆点增强算法分割出疑似肺结节区域,结合手动选取种子点确定明显肺结节。实现了肺部多组织的三维重建,可计算整体、局部(volume of interest,VOI)区域以及感兴趣CT值范围内的肺功能指标从而进行肺功能定量分析,并可在二维和三维上定位显示测算对象,结合多方面辅助医生诊断。展开更多
A new method is presented for the segmentation of pulmonary parenchyma. The proposed method is based on the area calculation of different objects in the image. The main purpose of the proposed algorithm is the segment...A new method is presented for the segmentation of pulmonary parenchyma. The proposed method is based on the area calculation of different objects in the image. The main purpose of the proposed algorithm is the segment of the lungs images from the computer tomography(CT) images. The original image is binarized using the bit-plane slicing technique and among the different images the best binarized image is chosen. After binarization, the labeling is done and the area of each label is calculated from which the next level of binarized image is obtained. Then, the boundary tracing algorithm is applied to get another level of binarized image. The proposed method is able to extract lung region from the original images. The experimental results show the significance of the proposed method.展开更多
文摘为了更加便利、快捷地在基于CT的肺部疾病诊断中获取相关肺功能指标,以便于辅助医生进行诊断,本研究开发了基于肺部组织分割的肺功能定量分析系统。系统主要分为三个部分:肺部组织的分割,肺部组织的三维重建以及肺功能定量分析模块。在利用已有方法分割出肺实质、肺气管和肺血管的基础上,提出了自动修补由肺结节造成的肺实质孔洞以及肺边缘缺陷的方法,利用基于海森矩阵的圆点增强算法分割出疑似肺结节区域,结合手动选取种子点确定明显肺结节。实现了肺部多组织的三维重建,可计算整体、局部(volume of interest,VOI)区域以及感兴趣CT值范围内的肺功能指标从而进行肺功能定量分析,并可在二维和三维上定位显示测算对象,结合多方面辅助医生诊断。
基金supported (in part) by research funding from Chosun University, Korea, 2013
文摘A new method is presented for the segmentation of pulmonary parenchyma. The proposed method is based on the area calculation of different objects in the image. The main purpose of the proposed algorithm is the segment of the lungs images from the computer tomography(CT) images. The original image is binarized using the bit-plane slicing technique and among the different images the best binarized image is chosen. After binarization, the labeling is done and the area of each label is calculated from which the next level of binarized image is obtained. Then, the boundary tracing algorithm is applied to get another level of binarized image. The proposed method is able to extract lung region from the original images. The experimental results show the significance of the proposed method.
